We consider a model describing Bose-Josephson junction (BJJ) coupled to a single bosonic mode exhibiting quantum phase transition (QPT). Onset of chaos above QPT is observed from semiclassical ...dynamics as well from spectral statistics. Based on entanglement entropy, we analyze the ergodic behavior of eigenstates with increasing energy density which also reveals the influence of dynamical steady state known as π-mode on it. We identify the imprint of unstable π-oscillation as many body quantum scar (MBQS), which leads to the deviation from ergodicity and quantify the degree of scarring. Persistence of phase coherence in nonequilibrium dynamics of such initial state corresponding to the π -mode is an observable signature of MBQS which has relevance in experiments on BJJ.
BH3 domains were originally discovered in the context of apoptosis regulators and they mediate binding of proapoptotic Bcl-2 family members to antiapoptotic Bcl-2 family members. Yet, recent studies ...indicate that BH3 domains do not function uniquely in apoptosis regulation; they also function in the regulation of another critical pathway involved in cellular and tissue homeostasis called autophagy. Antiapoptotic Bcl-2 homologs downregulate autophagy through interactions with the essential autophagy effector and haploinsufficient tumor suppressor, Beclin 1. Beclin 1 contains a BH3 domain, similar to that of Bcl-2 proteins, which is necessary and sufficient for binding to antiapoptotic Bcl-2 homologs and required for Bcl-2-mediated inhibition of autophagy. This review will summarize the evidence that the BH3 domain of Beclin 1 serves as a key structural motif that enables Bcl-2 to function not only as an antiapoptotic protein, but also as an antiautophagy protein.
With growing inclination towards the eco-friendly technology, natural fiber based polymer composite materials have been gaining a lot of momentum nowadays. The present review discusses the much ...research that has been carried out in the area of the epoxy-based composites reinforced with natural fibers. Influence of the various factors like the fiber content, fiber geometry, fiber size, surface treatment technique, and coupling agent on different properties like mechanical, thermal, behavior towards water absorption and others have been presented. It can be inferred that there is a need and scope for improvement of the surface properties of natural fiber using various methods like physical and chemical treatments, addition of coupling agents, etc. for the manufacturing of the composites having desired properties. These techniques not only modify surface morphology, but also improve other processing parameters like the hydrophilic character of fiber (which is desirable to be low), and hence improve several characteristics such as mechanical properties, thermal stability, water absorption and other considerations of the composites.
Recent experiments have revealed that beyond-mean-field corrections are much more relevant in weakly interacting dipolar condensates than in their nondipolar counterparts. We show that in ...quasi-one-dimensional geometries quantum corrections in dipolar and nondipolar condensates are strikingly different due to the peculiar momentum dependence of the dipolar interactions. The energy correction of the condensate presents not only a modified density dependence, but it may even change from attractive to repulsive at a critical density due to the surprising role played by the transversal directions. The anomalous quantum correction translates into a strongly modified physics for quantum-stabilized droplets and dipolar solitons. Moreover, and for similar reasons, quantum corrections of three-body correlations, and hence of three-body losses, are strongly modified by the dipolar interactions. This intriguing physics can be readily probed in current experiments with magnetic atoms.
A combination of silver nanoparticles (AgNPs) and an antibiotic can synergistically inhibit bacterial growth, especially against the drug-resistant bacteria Salmonella typhimurium. However, the ...mechanism for the synergistic activity is not known. This study chooses four classes of antibiotics, β-lactam (ampicillin and penicillin), quinolone (enoxacin), aminoglycoside (kanamycin and neomycin), and polykeptide (tetracycline) to explore their synergistic mechanism when combined with AgNPs against the multidrug-resistant bacterium Salmonella typhimurium DT 104. Enoxacin, kanamycin, neomycin, and tetracycline show synergistic growth inhibition against the Salmonella bacteria when combined with AgNPs, while ampicillin and penicillin do not. UV–vis and Raman spectroscopy studies reveal that all these four synergistic antibiotics can form complexes with AgNPs, while ampicillin and penicillin do not. The presence of tetracycline enhances the binding of Ag to Salmonella by 21% and Ag+ release by 26% in comparison to that without tetracycline, while the presence of penicillin does not enhance the binding of Ag or Ag+ release. This means that AgNPs first form a complex with tetracycline. The tetracycline–AgNPs complex interacts more strongly with the Salmonella cells and causes more Ag+ release, thus creating a temporal high concentration of Ag+ near the bacteria cell wall that leads to growth inhibition of the bacteria. These findings agree with the recent findings that Ag+ release from AgNPs is the agent causing toxicity.
Understanding how different physical and chemical atmospheric processes affect the formation of fine particles has been a persistent challenge. Inferring causal relations between the various measured ...features affecting the formation of secondary organic aerosol (SOA) particles is complicated since correlations between variables do not necessarily imply causality. Here, we apply a state-of-the-art information transfer measure coupled with the Koopman operator framework to infer causal relations between isoprene epoxydiol SOA (IEPOX-SOA) and different chemistry and meteorological variables derived from detailed regional model predictions over the Amazon rainforest. IEPOX-SOA represents one of the most complex SOA formation pathways and is formed by the interactions between natural biogenic isoprene emissions and anthropogenic emissions affecting sulfate, acidity and particle water. Since the regional model captures the known relations of IEPOX-SOA with different chemistry and meteorological features, their simulated time series implicitly include their causal relations. We show that our causal model successfully infers the known major causal relations between total particle phase 2-methyl tetrols (the dominant component of IEPOX-SOA over the Amazon) and input features. We provide the first proof of concept that the application of our causal model better identifies causal relations compared to correlation and random forest analyses performed over the same dataset. Our work has tremendous implications, as our methodology of causal discovery could be used to identify unknown processes and features affecting fine particles and atmospheric chemistry in the Earth's atmosphere.
One-dimensional quasiperiodic systems with power-law hopping, 1/r^{a}, differ from both the standard Aubry-André (AA) model and from power-law systems with uncorrelated disorder. Whereas in the AA ...model all single-particle states undergo a transition from ergodic to localized at a critical quasidisorder strength, short-range power-law hops with a>1 can result in mobility edges. We find that there is no localization for long-range hops with a≤1, in contrast to the case of uncorrelated disorder. Systems with long-range hops rather present ergodic-to-multifractal edges and a phase transition from ergodic to multifractal (extended but nonergodic) states. Both mobility and ergodic-to-multifractal edges may be clearly revealed in experiments on expansion dynamics.
In this paper, we provide a novel approach to capture causal interaction in a dynamical system from time series data. In Sinha and Vaidya (in: IEEE conference on decision and control, pp 7329–7334, ...2016), we have shown that the existing measures of information transfer, namely directed information, Granger causality and transfer entropy, fail to capture the causal interaction in a dynamical system and proposed a new definition of information transfer that captures direct causal interactions. The novelty of the information transfer definition used in this paper is the fact that it can differentiate between direct and indirect influences Sinha and Vaidya (2016). The main contribution of this paper is to show that the proposed definition of information transfers in Sinha and Vaidya (2016) and Sinha and Vaidya (in: Indian control conference, pp 303–308, 2017) can be computed from time series data, and thus, the direct influences in a dynamical system can be identified from time series data. We use transfer operator theoretic framework, involving Perron–Frobenius and Koopman operators for the data-driven approximation of the system dynamics and computation of information transfer. Several examples, involving linear and nonlinear system dynamics, are presented to verify the efficiency of the developed algorithm.
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